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Training-Based Multiple Source Tracking Using Manifold-Learning and recursive expectation-maximization

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 1124-1140页

作者： Bross, Avital Gannot, Sharon Bar Ilan Univ Fac Engn IL-5290002 Ramat Gan Israel

In this paper we propose a data-driven approach for multiple speaker tracking in reverberant enclosures. The speakers are uttering, possibly overlapping, speech signals while moving in the environment. The method comprises two stages. The first stage executes a single source localization using semi-supervised learning on multiple manifolds. The second stage, which is unsupervised, uses time-varying maximum likelihood estimation for tracking. The feature vectors, used by both stages, are the relative transfer functions (RTFs), which are known to be related to source positions. The number of sources is assumed to be known while the microphone positions are unknown. In the training stage, a large database of RTFs is given. A small percentage of the data is attributed with exact positions (namely, labelled data) and the rest is assumed to be unlabelled, i.e. the respective position is unknown. Then, a nonlinear, manifold-based, mapping function between the RTFs and the source positions is inferred. Applying this mapping function to all unlabelled RTFs constructs a dense grid of localized sources. In the test phase, this RTF grid serves as the centroids for a Mixture of Gaussians (MoG) model. The MoG parameters are estimated by applying a recursive variant of the expectation-maximization (EM) procedure that relies on the sparsity and intermittency of the speech signals. We present a comprehensive simulation study in various reverberation levels, including static and dynamic scenarios, for both two or three (partially) overlapping speakers. For the dynamic case we provide simulations with several speakers trajectories, including intersecting sources. The proposed scheme outperforms baseline methods that use a simpler propagation model in terms of localization accuracy and tracking capabilities.

关键词： Location awareness Microphones Acoustics Speech processing Hidden Markov models Manifolds Feature extraction Manifold learning multiple source tracking recursive expectation-maximization speech sparsity

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AN ONLINE MULTIPLE-SPEAKER DOA TRACKING USING THE CAPPE-MOULINES recursive expectation-maximization ALGORITHM 44

AN ONLINE MULTIPLE-SPEAKER DOA TRACKING USING THE CAPPE-MOUL...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Weisberg, Koby Gannot, Sharon Schwartz, Ofer Bar Ilan Univ Fac Engn Ramat Gan Israel CEVA DSP Audio Dept Herzliyya Israel

ISBN: (纸本)9781479981311

In this paper, we present a multiple-speaker direction of arrival (DOA) tracking algorithm with a microphone array that utilizes the recursive EM (REM) algorithm proposed by Cappe and Moulines. In our model, all sources can be located in one of a predefined set of candidate DOAs. Accordingly, the received signals from all microphones are modeled as Mixture of Gaussians (MoG) vectors in which each speaker is associated with a corresponding Gaussian. The localization task is then formulated as a maximum likelihood (ML) problem, where the MoG weights and the power spectral density (PSD) of the speakers are the unknown parameters. The REM algorithm is then utilized to estimate the ML parameters in an online manner, facilitating multiple source tracking. By using Fisher-Neyman factorization, the outputs of the minimum variance distortionless response (MVDR)-beamformer (BF) are shown to be sufficient statistics for estimating the parameters of the problem at hand. With that, the terms for the E-step are significantly simplified to a scalar form. An experimental study demonstrates the benefits of the using proposed algorithm in both a simulated data-set and real recordings from the acoustic source localization and tracking (LOCATA) data-set.

关键词： Speaker tracking recursive expectation-maximization LOCATA challenge

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Forward-backward recursive expectation-maximization for concurrent speaker tracking

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2021年第1期2021卷 2-2页

作者： Dorfan, Yuval Schwartz, Boaz Gannot, Sharon Bar Ilan Univ Fac Engn IL-5290002 Ramat Gan Israel

In this paper, a study addressing the task of tracking multiple concurrent speakers in reverberant conditions is presented. Since both past and future observations can contribute to the current location estimate, we propose a forward-backward approach, which improves tracking accuracy by introducing near-future data to the estimator, in the cost of an additional short latency. Unlike classical target tracking, we apply a non-Bayesian approach, which does not make assumptions with respect to the target trajectories, except for assuming a realistic change in the parameters due to natural behaviour. The proposed method is based on the recursive expectation-maximization (REM) approach. The new method is dubbed forward-backward recursive expectation-maximization (FB-REM). The performance is demonstrated using an experimental study, where the tested scenarios involve both simulated and recorded signals, with typical reverberation levels and multiple moving sources. It is shown that the proposed algorithm outperforms the regular common causal (REM).

关键词： Sound source tracking recursive expectation-maximization Microphone arrays Simultaneous speakers W-disjoint orthogonality Forward-backward

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State estimation for one-dimensional agro-hydrological processes with model mismatch

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第3期102卷 1122-1138页

作者： Liu, Zhuangyu Liu, Jinfeng Zhao, Shunyi Luan, Xiaoli Liu, Fei Jiangnan Univ Sch Internet Things Engn Wuxi Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China

The importance of accurate soil moisture data for the development of modern closed-loop irrigation systems cannot be overstated. Due to the diversity of soil, it is difficult to obtain an accurate model for the agro-hydrological system. In this study, soil moisture estimation in one-dimensional (1D) agro-hydrological systems with model mismatch is the focus. To address the problem of model mismatch, a nonlinear state-space model derived from the Richards equation is utilized, along with additive unknown inputs. The determination of the number of sensors required is achieved through sensitivity analysis and the orthogonalization projection method. To estimate states and unknown inputs in real-time, a recursive expectation maximization (EM) algorithm derived from the conventional EM algorithm is employed. During the E-step, the extended Kalman filter (EKF) is used to compute states and covariance in the recursive Q-function, while in the M-step, unknown inputs are updated by locally maximizing the recursive Q-function. The estimation performance is evaluated using comprehensive simulations. Through this method, accurate soil moisture estimation can be obtained, even in the presence of model mismatch.

关键词： 1D agro-hydrological systems extended Kalman filter model mismatch recursive expectation-maximization state estimation unknown inputs

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Online Multichannel Speech Enhancement Based on recursive EM and DNN-Based Speech Presence Estimation

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 3080-3094页

作者： Martin-Donas, Juan Manuel Jensen, Jesper Tan, Zheng-Hua Gomez, Angel M. Peinado, Antonio M. Univ Granada Dept Signal Theory Telemat & Commun Granada 18071 Spain Aalborg Univ Dept Elect Syst Sect Signal & Informat Proc SIP DK-9220 Aalborg Denmark Oticon AS DK-2765 Smorum Denmark Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

This article presents a recursive expectation-maximization algorithm for online multichannel speech enhancement. A deep neural network mask estimator is used to compute the speech presence probability, which is then improved by means of statistical spatial models of the noisy speech and noise signals. The clean speech signal is estimated using beamforming, single-channel linear postfiltering and speech presence masking. The clean speech statistics and speech presence probabilities are finally used to compute the acoustic parameters for beamforming and postfiltering by means of maximum likelihood estimation. This iterative procedure is carried out on a frame-by-frame basis. The algorithm integrates the different estimates in a common statistical framework suitable for online scenarios. Moreover, our method can successfully exploit spectral, spatial and temporal speech properties. Our proposed algorithm is tested in different noisy environments using the multichannel recordings of the CHiME-4 database. The experimental results show that our method outperforms other related state-of-the-art approaches in noise reduction performance, while allowing low-latency processing for real-time applications.

关键词： Speech enhancement Estimation Acoustics Noise measurement Computational modeling Array signal processing Deep neural networks Kalman filter multichannel speech enhancement recursive expectation-maximization speech presence probability

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Online Speech Dereverberation Using Kalman Filter and EM Algorithm

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2015年第2期23卷 394-406页

作者： Schwartz, Boaz Gannot, Sharon Habets, Emanuel A. P. Bar Ilan Univ Fac Engn IL-5290002 Ramat Gan Israel Joint Inst Univ Erlangen Nuremberg & Fraunhofer I Int Audio Labs Erlangen D-91058 Erlangen Germany

Speech signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown and time-varying. In this paper, a scenario with a single desired sound source and slowly time-varying and spatially-white noise is considered, and a multi-microphone algorithm that simultaneously estimates the clean speech signal and the time-varying acoustic system is proposed. The recursive expectation-maximization scheme is employed to obtain both the clean speech signal and the acoustic system in an online manner. In the expectation step, the Kalman filter is applied to extract a new sample of the clean signal, and in the maximization step, the system estimate is updated according to the output of the Kalman filter. Experimental results show that the proposed method is able to significantly reduce reverberation and increase the speech quality. Moreover, the tracking ability of the algorithm was validated in practical scenarios using human speakers moving in a natural manner.

关键词： Dereverberation recursive parameter estimation recursive expectation-maximization convolution in STFT

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Joint state and process inputs estimation for state-space models with Student's t-distribution

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2024年 253卷

作者： Ci, Hang Zhang, Chengxi Zhao, Shunyi Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China

This paper proposes a joint state and unknown inputs (UIs) discrete-time estimation method for industrial processes, represented by a state-space model. To cope with the outliers in process data, the measurement noise is characterized by the Student's t-distribution. The identification of UIs is accomplished through the recursive expectation-maximization (REM) approach. Specifically, in the E-step, a recursively calculated Qfunction is formulated by the maximum likelihood criterion, and the states and the variance scale factor are estimated iteratively. In the M-step, UIs are updated analytically together with the degree of freedom is updated approximately. The effectiveness of the proposed algorithm is validated using a quadruple water tank process and a continuous stirred tank reactor. It shows that the proposed method significantly enhances the robustness and estimation accuracy of state and UIs in industrial processes, effectively handling outliers and reducing computational demands for real-time applications.

关键词： Unknown inputs identification recursive expectation-maximization State estimation Kalman filter Student's t-distribution

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